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1,700 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Lesson One
This lesson will go over digital data input and introduction to programming in Python.
Sensors
If someone were to ask you what color an apple is, you would look at the apple and t... | Python Code:
print 40 + 2
print 7*6
print 67-25
print 798/19
Explanation: Lesson One
This lesson will go over digital data input and introduction to programming in Python.
Sensors
If someone were to ask you what color an apple is, you would look at the apple and tell them what color you saw. If they then asked you whic... |
1,701 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
2. Categorical Predictors
The syntax for handling categorical predictors is different between standard regression models/two-stage-models (i.e.
Step1: Dummy-coded/Treatment contrasts
+++++... | Python Code:
# import basic libraries and sample data
import os
import pandas as pd
from pymer4.utils import get_resource_path
from pymer4.models import Lm
# IV3 is a categorical predictors with 3 levels in the sample data
df = pd.read_csv(os.path.join(get_resource_path(), "sample_data.csv"))
Explanation: 2. Categorica... |
1,702 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
1. Import the necessary packages to read in the data, plot, and create a linear regression model
Step1: 2. Read in the hanford.csv file
Step2: County
Step3: 3. Calculate the basic descrip... | Python Code:
import pandas as pd
import pandas as pd
import matplotlib.pyplot as plt # package for doing plotting (necessary for adding the line)
import statsmodels.formula.api as smf # package we'll be using for linear regression
%matplotlib inline
Explanation: 1. Import the necessary packages to read in the data, plo... |
1,703 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Editing BEM surfaces in Blender
Sometimes when creating a BEM model the surfaces need manual correction because
of a series of problems that can arise (e.g. intersection between surfaces).
H... | Python Code:
# Authors: Marijn van Vliet <w.m.vanvliet@gmail.com>
# Ezequiel Mikulan <e.mikulan@gmail.com>
# Manorama Kadwani <manorama.kadwani@gmail.com>
#
# License: BSD-3-Clause
import os
import os.path as op
import shutil
import mne
data_path = mne.datasets.sample.data_path()
subjects_dir = op.joi... |
1,704 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<a href="https
Step1: OpenCV
OpenCV is an open-source computer vision library. It comes packaged with many powerful computer vision tools, including image and video processing utilities. Th... | Python Code:
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distribute... |
1,705 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
This model was developed by Permamodel workgroup.
Basic theory is Kudryavtsev's method.
Reference
Step1: Spatially visualize active layer thickness
Step2: Spatially visualize mean annual g... | Python Code:
import os,sys
sys.path.append('../../permamodel/')
from permamodel.components import bmi_Ku_component
from permamodel import examples_directory
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap, addcyclic
import matplotlib as mpl
print examples_d... |
1,706 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Run a Web Server in a Notebook
In this notebook, we show how to run a Tornado or Flask web server within a notebook, and access it from the public Internet. It sounds hacky, but the techniqu... | Python Code:
import matplotlib.pyplot as plt
import pandas as pd
import numpy
import io
pd.options.display.mpl_style = 'default'
def plot_random_numbers(n=50):
'''
Plot random numbers as a line graph.
'''
fig, ax = plt.subplots()
# generate some random numbers
arr = numpy.random.randn(n)
ax.... |
1,707 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Randomized LASSO
This selection algorithm allows the researcher to form a model
after observing the subgradient of this optimization problem
$$
\text{minimize}_{\beta} \frac{1}{2} \|y-X\bet... | Python Code:
import numpy as np
from selectinf.randomized.api import lasso
from selectinf.tests.instance import gaussian_instance
np.random.seed(0) # for reproducibility
X, y = gaussian_instance(n=100,
p=20,
s=5,
signal=3,
... |
1,708 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
update changes to pypi
```bash
update pypi
rm -r dist # remove old source files
python setup.py sdist # make source distribution
python setup.py bdist_wheel # make bui... | Python Code:
%ls dist
Explanation: update changes to pypi
```bash
update pypi
rm -r dist # remove old source files
python setup.py sdist # make source distribution
python setup.py bdist_wheel # make build distribution with .whl file
twine upload dist/ # pip install twine
```
End of explanat... |
1,709 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Class 18
Step1: Evaluation
Now we want to examine the statistical properties of the simulated model | Python Code:
# 1. Input model parameters and print
parameters = pd.Series()
parameters['rho'] = .75
parameters['sigma'] = 0.006
parameters['alpha'] = 0.35
parameters['delta'] = 0.025
parameters['beta'] = 0.99
print(parameters)
# 2. Compute the steady state of the model directly
A = 1
K = (parameters.alpha*A/(parameters... |
1,710 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Tutorial on Causal Inference and its Connections to Machine Learning (Using DoWhy+EconML)
This tutorial presents a walk-through on using DoWhy+EconML libraries for causal inference. Along th... | Python Code:
# Required libraries
import dowhy
from dowhy import CausalModel
import dowhy.datasets
# Avoiding unnecessary log messges and warnings
import logging
logging.getLogger("dowhy").setLevel(logging.WARNING)
import warnings
from sklearn.exceptions import DataConversionWarning
warnings.filterwarnings(action='igno... |
1,711 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Derivatives fundamentals
This notebook will introduce you to the fundamentals of computing the derivative of the solution map to optimization problems. The derivative can be used for sensitv... | Python Code:
import cvxpy as cp
x = cp.Variable(pos=True)
y = cp.Variable(pos=True)
z = cp.Variable(pos=True)
a = cp.Parameter(pos=True)
b = cp.Parameter(pos=True)
c = cp.Parameter()
objective_fn = 1/(x*y*z)
objective = cp.Minimize(objective_fn)
constraints = [a*(x*y + x*z + y*z) <= b, x >= y**c]
problem = cp.Problem(o... |
1,712 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Please find jax implementation of this notebook here
Step1: Model
We use a slightly modified version of the LeNet CNN.
Step2: Copying parameters across devices
Step4: All-reduce will copy... | Python Code:
import numpy as np
import matplotlib.pyplot as plt
import math
from IPython import display
try:
import torch
except ModuleNotFoundError:
%pip install -qq torch
import torch
try:
import torchvision
except ModuleNotFoundError:
%pip install -qq torchvision
import torchvision
from torch... |
1,713 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Baseline prediction for homework type
The baseline prediction method we use for predicting which homework the notebook came from uses the popular plagiarism detector JPlag.
We feed each note... | Python Code:
# First step is to load a balanced dataset of homeworks
import sys
home_directory = '/dfs/scratch2/fcipollone'
sys.path.append(home_directory)
import numpy as np
from nbminer.notebook_miner import NotebookMiner
hw_filenames = np.load('../homework_names_jplag_combined_per_student.npy')
min_val = min([len(te... |
1,714 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Factor Risk Exposure
By Evgenia "Jenny" Nitishinskaya, Delaney Granizo-Mackenzie, and Maxwell Margenot.
Part of the Quantopian Lecture Series
Step2: How did each factor do over 2014?... | Python Code:
import numpy as np
import statsmodels.api as sm
import scipy.stats as stats
from statsmodels import regression
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from quantopian.pipeline import Pipeline
from quantopian.pipeline.data import morningstar
from quantopian.pipeline.data.built... |
1,715 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Experiment
Run Hebbian pruning with non-binary activations.
Motivation
Attempt pruning given intuition offered up in "Memory Aware Synapses" paper
Step1: Dense Model
Step2: Static Sparse
S... | Python Code:
from IPython.display import Markdown, display
%load_ext autoreload
%autoreload 2
import sys
import itertools
sys.path.append("../../")
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import glob
import tabulate
import pprint
import clic... |
1,716 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Using nbtlib
The Named Binary Tag (NBT) file format is a simple structured binary format that is mainly used by the game Minecraft (see the official specification for more details). This sho... | Python Code:
import nbtlib
nbt_file = nbtlib.load('nbt_files/bigtest.nbt')
nbt_file['stringTest']
Explanation: Using nbtlib
The Named Binary Tag (NBT) file format is a simple structured binary format that is mainly used by the game Minecraft (see the official specification for more details). This short documentation wi... |
1,717 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<img src="http
Step1: Risk Factor Models
The first step is to define a model for the risk-neutral discounting.
Step2: We then define a market environment containing the major parameter spe... | Python Code:
import dx
import datetime as dt
import pandas as pd
import seaborn as sns; sns.set()
Explanation: <img src="http://hilpisch.com/tpq_logo.png" alt="The Python Quants" width="45%" align="right" border="4">
Quickstart
This brief first part illustrates---without much explanation---the usage of the DX Analytics... |
1,718 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Streaming Sourmash
This notebook demonstrates how to use goetia to perform a streaming analysis of sourmash minhash signatures. Goetia includes the sourmash C++ header and exposes it with cp... | Python Code:
# First, import the necessary libraries
from goetia import libgoetia
from goetia.alphabets import DNAN_SIMPLE
from goetia.signatures import SourmashSignature
from sourmash import load_one_signature, MinHash
import screed
from ficus import FigureManager
import seaborn as sns
import numpy as np
Explanation: ... |
1,719 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Bandicoot
bandicoot is an open-source python toolbox to analyze mobile phone metadata.
For more information, see
Step1: Input files
<img src="mini-mockups-01.png" width="80%" style="border
... | Python Code:
%pylab inline
import seaborn as sns
Explanation: Bandicoot
bandicoot is an open-source python toolbox to analyze mobile phone metadata.
For more information, see: http://bandicoot.mit.edu/
<hr>
End of explanation
!head -n 5 data/ego.csv
!head -n 5 data/antennas.csv
Explanation: Input files
<img src="mini-m... |
1,720 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Attention Based Classification Tutorial
Recommended time
Step1: Load & Explore Data
Let's begin by downloading the data from Figshare and cleaning and splitting it for use in training.
Step... | Python Code:
%load_ext autoreload
%autoreload 2
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import pandas as pd
import tensorflow as tf
import numpy as np
import time
import os
from sklearn import metrics
from visualize_attention import attentionDisplay
f... |
1,721 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Artificial Intelligence & Machine Learning
Ujian Akhir Semester
Mekanisme
Anda hanya diwajibkan untuk mengumpulkan file ini saja ke uploader yang disediakan di https
Step1: Soal 1.2.a (2 po... | Python Code:
import networkx as nx
# Kode Anda di sini
Explanation: Artificial Intelligence & Machine Learning
Ujian Akhir Semester
Mekanisme
Anda hanya diwajibkan untuk mengumpulkan file ini saja ke uploader yang disediakan di https://elearning.uai.ac.id/. Ganti nama file ini saat pengumpulan menjadi uas_NIM.ipynb.
Ke... |
1,722 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<h1 align="center">Regression with Categorical Variables</h1>
Step2: The BirthSmokers Data
Researchers interested in answering the above research question collected the following data (birt... | Python Code:
%pylab inline
pylab.style.use('ggplot')
import numpy as np
import pandas as pd
Explanation: <h1 align="center">Regression with Categorical Variables</h1>
End of explanation
smoking_txt = Wgt Gest Smoke
2940 38 yes
3130 38 no
2420 36 yes
2450 34 no
2760 39 yes
2440 35 yes
3226 40 no
3301 42 yes
2729 37 no
3... |
1,723 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Flux sampling
Basic usage
The easiest way to get started with flux sampling is using the sample function in the flux_analysis submodule. sample takes at least two arguments
Step1: By defaul... | Python Code:
from cobra.test import create_test_model
from cobra.flux_analysis import sample
model = create_test_model("textbook")
s = sample(model, 100)
s.head()
Explanation: Flux sampling
Basic usage
The easiest way to get started with flux sampling is using the sample function in the flux_analysis submodule. sample ... |
1,724 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Opening and previewing
This uses the tiny excel spreadsheet example1.xls. It is small enough to preview inline in this notebook. But for bigger spreadsheet tables you will want to open the... | Python Code:
# Load in the functions
from databaker.framework import *
# Load the spreadsheet
tabs = loadxlstabs("example1.xls")
# Select the first table
tab = tabs[0]
print("The unordered bag of cells for this table looks like:")
print(tab)
Explanation: Opening and previewing
This uses the tiny excel spreadsheet examp... |
1,725 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Python for Bioinformatics
This Jupyter notebook is intented to be used alongside the book Python for Bioinformatics
Chapter 5
Step1: Once the previous command are executed, you can open the... | Python Code:
!curl https://raw.githubusercontent.com/Serulab/Py4Bio/master/samples/samples.tar.bz2 -o samples.tar.bz2
!mkdir samples
!tar xvfj samples.tar.bz2 -C samples
Explanation: Python for Bioinformatics
This Jupyter notebook is intented to be used alongside the book Python for Bioinformatics
Chapter 5: Handling F... |
1,726 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Немного безумия
смотрим, работают ли attention models и другие модели
Step2: Вспомогательные функции
Step3: Графики
Step4: Просто поезд + inclusive
Step5: Для сравнения
фильтрация выборк... | Python Code:
%matplotlib inline
from matplotlib import pyplot as plt
import numpy
import root_numpy
# import pandas - no pandas today
from astropy.table import Table
from sklearn.metrics import roc_auc_score
from scipy.special import logit
from decisiontrain import DecisionTrainClassifier
from collections import Order... |
1,727 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
SFR package example
Demonstrates functionality of Flopy SFR module using the example documented by Prudic and others (2004)
Step1: copy over the example files to the working directory
Step2... | Python Code:
import sys
import platform
import os
import numpy as np
import glob
import shutil
import matplotlib as mpl
import matplotlib.pyplot as plt
import flopy
import flopy.utils.binaryfile as bf
#Set name of MODFLOW exe
# assumes executable is in users path statement
exe_name = 'mf2005'
if platform.system() == '... |
1,728 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Getting Started
Tensors are similar to numpy's ndarrays, with the addition being that Tensors can also be used on a GPU to accelerate computing.
Step1: Numpy Bridge
The torch Tensor and num... | Python Code:
x = torch.Tensor(5, 3); x
x = torch.rand(5, 3); x
x.size()
y = torch.rand(5, 3)
x + y
torch.add(x, y)
result = torch.Tensor(5, 3)
torch.add(x, y, out=result)
result1 = torch.Tensor(5, 3)
result1 = x + y
result1
# anything ending in '_' is an in-place operation
y.add_(x) # adds x to y in-place
# standard nu... |
1,729 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Last updated
Step1: 1. Loading data
More details, see http
Step2: From a local text file
Let's first load some temperature data which covers all lattitudes. Since read_table is supposed to... | Python Code:
%matplotlib inline
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from pandas import set_option
set_option("display.max_rows", 16)
LARGE_FIGSIZE = (12, 8)
# Change this cell to the demo location on YOUR machine
%cd 'D:\\Git\\Pandas_Tutorial\\demos\\climate_timeseries'
%ls
Explanatio... |
1,730 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Sentiment Classification & How To "Frame Problems" for a Neural Network
by Andrew Trask
Twitter
Step1: Note
Step2: Lesson
Step3: Project 1
Step4: We'll create three Counter objects, one ... | Python Code:
def pretty_print_review_and_label(i):
print(labels[i] + "\t:\t" + reviews[i][:80] + "...")
g = open('reviews.txt','r') # What we know!
reviews = list(map(lambda x:x[:-1],g.readlines()))
g.close()
g = open('labels.txt','r') # What we WANT to know!
labels = list(map(lambda x:x[:-1].upper(),g.readlines())... |
1,731 | Given the following text description, write Python code to implement the functionality described.
Description:
Count minimum factor jumps required to reach the end of an Array
vector to store factors of each integer ; dp array ; Precomputing all factors of integers from 1 to 100000 ; Function to count the minimum jumps... | Python Code:
factors =[[ ] for i in range(100005 ) ] ;
dp =[0 for i in range(100005 ) ] ;
def precompute() :
for i in range(1 , 100001 ) :
for j in range(i , 100001 , i ) :
factors[j ] . append(i ) ;
def solve(arr , k , n ) :
if(k == n - 1 ) :
return 0 ;
if(k >= n ) :
return 1000000000... |
1,732 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Ch5 Categorizing and Tagging Words
本章的目標是回答這些問題
Step1: 上面的範例中，CC是對等連接詞、RB是副詞、IN是介系詞、NN是名詞、JJ則是形容詞。如果想知道詳細的tag定義，可以用nltk.help.upenn_tagset('RB')來查詢。
Tagged Corpora
在NLTK的習慣上，tagged token會表示成... | Python Code:
import nltk
text = nltk.word_tokenize("And now for something completely different")
nltk.pos_tag(text)
Explanation: Ch5 Categorizing and Tagging Words
本章的目標是回答這些問題:
什麼是lexical categories? 它們如何應用在NLP中?
要儲存單字和分類的資料結構是什麼?
如何自動為每個單字分類?
本章會提到一些基本的NLP方法，例如sequence labeling、n-gram models、backoff、evaluation。
辨識單字的... |
1,733 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Automate the ML process using pipelines
There are standard workflows in a machine learning project that can be automated. In Python scikit-learn, Pipelines help to clearly define and automat... | Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
# Create a pipeline that standardizes the data then creates a model
#Load libraries for data processing
import pandas as pd #data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
from scipy.stats import norm
from sklearn.model_selection impor... |
1,734 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Table of Contents
<p><div class="lev1 toc-item"><a href="#Chapter-1" data-toc-modified-id="Chapter-1-1"><span class="toc-item-num">1 </span>Chapter 1</a></div><div class="lev2 toc... | Python Code:
from graphviz import Digraph
dot = Digraph('Ex 1.4')
dot.edge('Coin 1', 'Ball 1')
dot.edge('Coin 2', 'Ball 2')
dot.edge('Ball 1', 'Sample 1=red')
dot.edge('Ball 2', 'Sample 1=red')
dot.edge('Ball 1', 'Sample 2=red')
dot.edge('Ball 2', 'Sample 2=red')
dot.edge('Ball 1', 'Sample 3=red')
dot.edge('Ball 2', 'S... |
1,735 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
The business ID field has already been filtered for only restaurants
We want to filter the users collection for the following
Step1: Create a new dictionary with the following structure and... | Python Code:
#Find a list of users with at least 20 reviews
user_list = []
for user in users.find():
if user['review_count'] >= 20:
user_list.append(user['_id'])
else:
pass
Explanation: The business ID field has already been filtered for only restaurants
We want to filter the users collection fo... |
1,736 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Think Bayes
Step1: Improving Reading Ability
From DASL(http
Step2: And use groupby to compute the means for the two groups.
Step4: The Normal class provides a Likelihood function that com... | Python Code:
from __future__ import print_function, division
% matplotlib inline
import warnings
warnings.filterwarnings('ignore')
import math
import numpy as np
from thinkbayes2 import Pmf, Cdf, Suite, Joint, EvalBinomialPmf
import thinkplot
Explanation: Think Bayes: Chapter 9
This notebook presents code and exercises... |
1,737 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step2: OT for image color adaptation
This example presents a way of transferring colors between two images
with Optimal Transport as introduced in [6]
[6] Ferradans, S., Papadakis, N., Peyre... | Python Code:
# Authors: Remi Flamary <remi.flamary@unice.fr>
# Stanislas Chambon <stan.chambon@gmail.com>
#
# License: MIT License
import numpy as np
from scipy import ndimage
import matplotlib.pylab as pl
import ot
r = np.random.RandomState(42)
def im2mat(I):
Converts an image to matrix (one pixel per lin... |
1,738 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Fourier analysis & resonances
A great benefit of being able to call rebound from within python is the ability to directly apply sophisticated analysis tools from scipy and other python libra... | Python Code:
import rebound
import numpy as np
sim = rebound.Simulation()
sim.units = ('AU', 'yr', 'Msun')
sim.add("Sun")
sim.add("Jupiter")
sim.add("Saturn")
Explanation: Fourier analysis & resonances
A great benefit of being able to call rebound from within python is the ability to directly apply sophisticated analys... |
1,739 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2019 The TensorFlow Hub Authors.
Licensed under the Apache License, Version 2.0 (the "License");
Step1: 使用近似最近邻和文本嵌入向量构建语义搜索
<table class="tfo-notebook-buttons" align="left">
<t... | Python Code:
# Copyright 2018 The TensorFlow Hub Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless re... |
1,740 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<!--
The ipynb was auto-generated from markdown using notedown.
Instead of modifying the ipynb file modify the markdown source.
-->
<h1 class="tocheading">Spark</h1>
<div id="toc"></div>
<... | Python Code:
from pyspark import SparkContext
sc = SparkContext()
Explanation: <!--
The ipynb was auto-generated from markdown using notedown.
Instead of modifying the ipynb file modify the markdown source.
-->
<h1 class="tocheading">Spark</h1>
<div id="toc"></div>
<img src="images/spark-logo.png">
Apache Spark
Spark... |
1,741 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Performing a full distance comparison using PSA
In this example, PSA is used to compute the mutual pairwise distances between a set of trajectories. In this notebook, we show how to perform ... | Python Code:
%matplotlib inline
%load_ext autoreload
%autoreload 2
# Suppress FutureWarning about element-wise comparison to None
# Occurs when calling PSA plotting functions
import warnings
warnings.filterwarnings('ignore')
Explanation: Performing a full distance comparison using PSA
In this example, PSA is used to co... |
1,742 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Compute source power estimate by projecting the covariance with MNE
We can apply the MNE inverse operator to a covariance matrix to obtain
an estimate of source power. This is computationall... | Python Code:
# Author: Denis A. Engemann <denis-alexander.engemann@inria.fr>
# Luke Bloy <luke.bloy@gmail.com>
#
# License: BSD-3-Clause
import os.path as op
import numpy as np
import mne
from mne.datasets import sample
from mne.minimum_norm import make_inverse_operator, apply_inverse_cov
data_path = sample.dat... |
1,743 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Multiple Traveling Salesman and the Problem of routing vehicles
Imagine we have instead of one salesman traveling to all the sites, that instead the workload is shared among many salesman. T... | Python Code:
from pulp import *
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sn
Explanation: Multiple Traveling Salesman and the Problem of routing vehicles
Imagine we have instead of one salesman traveling to all the sites, that instead the workload is shared among many sales... |
1,744 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Naive Bayes Male or Female Multivariate
author
Step1: Since we are simply using two Multivariate Gaussian Distributions, our Naive Bayes model is very simple to initialize.
Step2: Of cours... | Python Code:
from pomegranate import *
import numpy as np
Explanation: Naive Bayes Male or Female Multivariate
author: Nicholas Farn [<a href="sendto:nicholasfarn@gmail.com">nicholasfarn@gmail.com</a>]
This example shows how to create a Multivariate Guassian Naive Bayes Classifier using pomegranate. In this example we ... |
1,745 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Graded = 10/11
Homework #4
These problem sets focus on list comprehensions, string operations and regular expressions.
Problem set #1
Step1: In the following cell, complete the code with an... | Python Code:
numbers_str = '496,258,332,550,506,699,7,985,171,581,436,804,736,528,65,855,68,279,721,120'
Explanation: Graded = 10/11
Homework #4
These problem sets focus on list comprehensions, string operations and regular expressions.
Problem set #1: List slices and list comprehensions
Let's start with some data. The... |
1,746 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Title
Step1: Generate Features And Target Data
Step2: Create Logistic Regression
Step3: Cross-Validate Model Using Precision | Python Code:
# Load libraries
from sklearn.model_selection import cross_val_score
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import make_classification
Explanation: Title: Precision
Slug: precision
Summary: How to evaluate a Python machine learning using precision.
Date: 2017-09-15 12:0... |
1,747 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
For high dpi displays.
Step1: 0. General note
This example compares pressure calculated from pytheos and original publication for the gold scale by Dorogokupets 2007.
1. Global setup
Step2:... | Python Code:
%config InlineBackend.figure_format = 'retina'
Explanation: For high dpi displays.
End of explanation
import matplotlib.pyplot as plt
import numpy as np
from uncertainties import unumpy as unp
import pytheos as eos
Explanation: 0. General note
This example compares pressure calculated from pytheos and orig... |
1,748 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Testing averaging methods
From this post
The equation is
Step2: $$\frac{\partial\phi}{\partial t}+\nabla . \left(-D\left(\phi_{0}\right)\nabla \phi\right)+\nabla.\left(-\nabla \phi_{... | Python Code:
from fipy import Grid2D, CellVariable, FaceVariable
import numpy as np
def upwindValues(mesh, field, velocity):
Calculate the upwind face values for a field variable
Note that the mesh.faceNormals point from `id1` to `id2` so if velocity is in the same
direction as the `faceNormal`s then we tak... |
1,749 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Trees and Forests
NOTE
Step1: Decision Tree Classification
Step2: Random Forests
Step3: Selecting the Optimal Estimator via Cross-Validation
Step4: Fit the forest manually | Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
Explanation: Trees and Forests
NOTE: This module code was partly taken from Andreas Muellers Adavanced scikit-learn O'Reilly Course
It is just used to explore the scikit-learn random forest object in a systematic manner
I've added more c... |
1,750 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Intro to Cython
Why Cython
Outline
Step1: Now, let's time this
Step2: Not too bad, but this can add up. Let's see if Cython can do better
Step3: That's a little bit faster, which is nice ... | Python Code:
def f(x):
y = x**4 - 3*x
return y
def integrate_f(a, b, n):
dx = (b - a) / n
dx2 = dx / 2
s = f(a) * dx2
for i in range(1, n):
s += f(a + i * dx) * dx
s += f(b) * dx2
return s
Explanation: Intro to Cython
Why Cython
Outline:
Speed up Python code
Interact with Nu... |
1,751 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
PyLadies and local Python User Groups
Last updated
Step1: Part 1
Step2: The Meetup API limits requests, however their documentation isn't exactly helpful. Using their headers, I saw that ... | Python Code:
from __future__ import print_function
from collections import defaultdict
import json
import os
import time
import requests
Explanation: PyLadies and local Python User Groups
Last updated: August 4, 2015
I am not a statistician by trade; far from it. I did take a few stats & econometrics courses in colleg... |
1,752 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
GPyTorch Regression Tutorial
Introduction
In this notebook, we demonstrate many of the design features of GPyTorch using the simplest example, training an RBF kernel Gaussian process on a si... | Python Code:
import math
import torch
import gpytorch
from matplotlib import pyplot as plt
%matplotlib inline
%load_ext autoreload
%autoreload 2
Explanation: GPyTorch Regression Tutorial
Introduction
In this notebook, we demonstrate many of the design features of GPyTorch using the simplest example, training an RBF ker... |
1,753 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Linear algebra
Step1: Matrix and vector products
Q1. Predict the results of the following code.
Step2: Q2. Predict the results of the following code.
Step3: Q3. Predict the results of the... | Python Code:
import numpy as np
np.__version__
Explanation: Linear algebra
End of explanation
x = [1,2]
y = [[4, 1], [2, 2]]
print np.dot(x, y)
print np.dot(y, x)
print np.matmul(x, y)
print np.inner(x, y)
print np.inner(y, x)
Explanation: Matrix and vector products
Q1. Predict the results of the following code.
End of... |
1,754 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Inverse Kinematics Problem
In this example, we are going to use the pyswarms library to solve a 6-DOF (Degrees of Freedom) Inverse Kinematics (IK) problem by treating it as an optimization p... | Python Code:
# Import modules
import numpy as np
# Import PySwarms
import pyswarms as ps
# Some more magic so that the notebook will reload external python modules;
# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython
%load_ext autoreload
%autoreload 2
Explanation: Inverse Kinematics Proble... |
1,755 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Text classification with a RNN Tutorial in Tensorflow 2.0
Step1: Set up input pipeline
The IMDB large movie review dataset is a binary classification dataset—all the reviews have either a p... | Python Code:
import tensorflow as tf
import tensorflow_datasets as tfds
import matplotlib.pyplot as plt
import time
Explanation: Text classification with a RNN Tutorial in Tensorflow 2.0
End of explanation
dataset, info = tfds.load("imdb_reviews/subwords8k", with_info=True, as_supervised=True)
train_dataset, test_datas... |
1,756 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Introducción a Python para Ciencias Biólogicas
Curso de Biofísica - Universidad de Antioquia
Daniel Mejía Raigosa (email
Step1: Probemos creando una variable que inicialice a una palabra
Es... | Python Code:
print("Hola mundo!")
print("1+1=",2)
print("Hola, otra vez","1+1=",2)
print("Hola, otra vez.","Sabias que 1+1 =",2,"?")
numero=3
print(numero)
numero=3.1415
print(numero)
Explanation: Introducción a Python para Ciencias Biólogicas
Curso de Biofísica - Universidad de Antioquia
Daniel Mejía Raigosa (email: d... |
1,757 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Outlier Detection with bqplot
In this notebook, we create a class DNA that leverages the new bqplot canvas based HeatMap along with the ipywidgets Range Slider to help us detect and c... | Python Code:
from bqplot import (
DateScale,
ColorScale,
HeatMap,
Figure,
LinearScale,
OrdinalScale,
Axis,
)
from scipy.stats import percentileofscore
from scipy.interpolate import interp1d
import bqplot.pyplot as plt
from traitlets import List, Float, observe
from ipywidgets import IntRange... |
1,758 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Accessing and Plotting Meshes
Setup
Let's first make sure we have the latest version of PHOEBE 2.1 installed. (You can comment out this line if you don't use pip for your installation or don... | Python Code:
!pip install -I "phoebe>=2.1,<2.2"
Explanation: Accessing and Plotting Meshes
Setup
Let's first make sure we have the latest version of PHOEBE 2.1 installed. (You can comment out this line if you don't use pip for your installation or don't want to update to the latest release).
End of explanation
%matplot... |
1,759 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<H1>Multivariate regression</H1>
Step1: Let's evaluate how much the membrane potential depends on Input resistance and
membrane time constant and the sag ratio. We will create the followin... | Python Code:
%pylab inline
import pandas as pd
mypath = 'Cell_types.xlsx'
xls = pd.read_excel(mypath)
xls.head()
xls.InputR
xls['Vrest'].mean()
xls['Vrest'].unique() # get NumPy array
Explanation: <H1>Multivariate regression</H1>
End of explanation
x = xls[['InputR', 'SagRatio','mbTau']]
y = xls[['Vrest']]
# import sta... |
1,760 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Cross Validation
Step1: cross_val_score uses the KFold or StratifiedKFold strategies by default
Step2: Cross Validation Iterator
K-Fold - KFold divides all the samples in k groups of ... | Python Code:
# import
from sklearn.datasets import load_iris
from sklearn.cross_validation import cross_val_score, KFold, train_test_split, cross_val_predict, LeaveOneOut, LeavePOut
from sklearn.cross_validation import ShuffleSplit, StratifiedKFold, StratifiedShuffleSplit
from sklearn.metrics import accuracy_score
from... |
1,761 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<small><i>This notebook was put together by Jake Vanderplas. Source and license info is on GitHub.</i></small>
Density Estimation
Step1: Introducing Gaussian Mixture Models
We previously sa... | Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from scipy import stats
plt.style.use('seaborn')
Explanation: <small><i>This notebook was put together by Jake Vanderplas. Source and license info is on GitHub.</i></small>
Density Estimation: Gaussian Mixture Models
Here we'll explore G... |
1,762 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ES-DOC CMIP6 Model Properties - Atmoschem
MIP Era
Step1: Document Authors
Set document authors
Step2: Document Contributors
Specify document contributors
Step3: Document Publication
Speci... | Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'nims-kma', 'sandbox-2', 'atmoschem')
Explanation: ES-DOC CMIP6 Model Properties - Atmoschem
MIP Era: CMIP6
Institute: NIMS-KMA
Source ID: SANDBOX-2
Topic: Atmoschem
Sub-Topics: Transp... |
1,763 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
A Simple Autoencoder
We'll start off by building a simple autoencoder to compress the MNIST dataset. With autoencoders, we pass input data through an encoder that makes a compressed represen... | Python Code:
%matplotlib inline
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', validation_size=0)
Explanation: A Simple Autoencoder
We'll start off by building a simple autoencoder to c... |
1,764 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Tutorial Part 5
Step1: There are actually two different approaches you can take to using TensorFlow or PyTorch models with DeepChem. It depends on whether you want to use TensorFlow/PyTorc... | Python Code:
!curl -Lo conda_installer.py https://raw.githubusercontent.com/deepchem/deepchem/master/scripts/colab_install.py
import conda_installer
conda_installer.install()
!/root/miniconda/bin/conda info -e
!pip install --pre deepchem
Explanation: Tutorial Part 5: Creating Models with TensorFlow and PyTorch
In the t... |
1,765 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
You are currently looking at version 1.0 of this notebook. To download notebooks and datafiles, as well as get help on Jupyter notebooks in the Coursera platform, visit the Jupyter Notebook ... | Python Code:
%matplotlib notebook
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_digits
dataset = load_digits()
X, y = dataset.data, dataset.target
for class_name, class_count in zip(data... |
1,766 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
The intensity is $\theta * X$ where $X$ is a row vector.
Step1: We consider different shapes for the intensity | Python Code:
theta = np.array([2])
Explanation: The intensity is $\theta * X$ where $X$ is a row vector.
End of explanation
X = 0.1*np.random.normal(size = (d,N))
X = np.reshape(np.ones(N,),(1,N))
X = np.reshape(np.sin(np.arange(N)),(1,N))
dt = 0.1 # discretization step
l = np.exp(np.dot(X.T,theta))
u = np.random.unifo... |
1,767 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Plot the histogram of the number of trajectories over queries.
Step1: Plot the histogram of the length of trajectory given a start point.
Step2: Compute the ratio of multi-label when query... | Python Code:
plt.figure(figsize=[15, 5])
ax = plt.subplot()
ax.set_xlabel('#Trajectories')
ax.set_ylabel('#Queries')
ax.set_title('Histogram of #Trajectories')
queries = sorted(dat_obj.TRAJID_GROUP_DICT.keys())
X = [len(dat_obj.TRAJID_GROUP_DICT[q]) for q in queries]
pd.Series(X).hist(ax=ax, bins=20)
Explanation: Plot ... |
1,768 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Big Data Applications and Analytics - Term Project
Sean M. Shiverick Fall 2017
Classification of Prescription Opioid Misuse
Step1: Part 1. Load Project dataset
Delete first two columns and ... | Python Code:
import sklearn
import mglearn
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
Explanation: Big Data Applications and Analytics - Term Project
Sean M. Shiverick Fall 2017
Classification of Prescription Opioid Misuse: PRL
Logistic Regression Classifier, Decision Tree... |
1,769 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
LAB 2b
Step1: Import necessary libraries.
Step2: Lab Task #1
Step3: The source dataset
Our dataset is hosted in BigQuery. The CDC's Natality data has details on US births from 1969 to 200... | Python Code:
%%bash
sudo pip freeze | grep google-cloud-bigquery==1.6.1 || \
sudo pip install google-cloud-bigquery==1.6.1
Explanation: LAB 2b: Prepare babyweight dataset.
Learning Objectives
Setup up the environment
Preprocess natality dataset
Augment natality dataset
Create the train and eval tables in BigQuery
Expo... |
1,770 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Handwritten Number Recognition with TFLearn and MNIST
In this notebook, we'll be building a neural network that recognizes handwritten numbers 0-9.
This kind of neural network is used in a ... | Python Code:
# Import Numpy, TensorFlow, TFLearn, and MNIST data
import numpy as np
import tensorflow as tf
import tflearn
import tflearn.datasets.mnist as mnist
Explanation: Handwritten Number Recognition with TFLearn and MNIST
In this notebook, we'll be building a neural network that recognizes handwritten numbers 0-... |
1,771 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Correlation Matrix
By calling df.corr() on a full pandas DataFrame will return a square matrix containing all pairs of correlations.
By plotting them as a heatmap, you can visualize many cor... | Python Code:
df = x_plus_noise(randomness=0)
sns.heatmap(df.corr(), vmin=0, vmax=1)
df.corr()
Explanation: Correlation Matrix
By calling df.corr() on a full pandas DataFrame will return a square matrix containing all pairs of correlations.
By plotting them as a heatmap, you can visualize many correlations more efficien... |
1,772 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Gravitational Redshift (rv_grav)
Setup
Let's first make sure we have the latest version of PHOEBE 2.3 installed (uncomment this line if running in an online notebook session such as colab).
... | Python Code:
#!pip install -I "phoebe>=2.3,<2.4"
Explanation: Gravitational Redshift (rv_grav)
Setup
Let's first make sure we have the latest version of PHOEBE 2.3 installed (uncomment this line if running in an online notebook session such as colab).
End of explanation
import phoebe
from phoebe import u # units
import... |
1,773 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Algorithms Exercise 3
Imports
Step2: Character counting and entropy
Write a function char_probs that takes a string and computes the probabilities of each character in the string
Step4: Th... | Python Code:
%matplotlib inline
from matplotlib import pyplot as plt
import numpy as np
from IPython.html.widgets import interact
Explanation: Algorithms Exercise 3
Imports
End of explanation
def char_probs(s):
Find the probabilities of the unique characters in the string s.
Parameters
----------
s... |
1,774 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
The notebook interface
The IPython -- being rebranded as Jupyter -- notebook interface is becoming a standard for a number of languages other than Python
Step1: For instance, you can benchm... | Python Code:
%quickref
Explanation: The notebook interface
The IPython -- being rebranded as Jupyter -- notebook interface is becoming a standard for a number of languages other than Python: Julia, Scala, R, Haskell, bash are all getting their kernels in IPython. Since Python allows you to call MATLAB anyway, you can a... |
1,775 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Peak finder
Can one break a peak into several gaussian peaks usding pymc?
Step1: Simulate data
Step2: Now we know that the answer two overlayed gaussians. So model it that way and see what... | Python Code:
# http://onlinelibrary.wiley.com/doi/10.1002/2016JA022652/epdf
import datetime
import pymc
from pprint import pprint
import numpy as np
import matplotlib.pyplot as plt
import spacepy.plot as spp
print(datetime.datetime.now().isoformat())
Explanation: Peak finder
Can one break a peak into several gaussian p... |
1,776 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Reading an event file
Read events from a file. For a more detailed guide on how to read events
using MNE-Python, see tut_epoching_and_averaging.
Step1: Reading events
Below we'll read in an... | Python Code:
# Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Chris Holdgraf <choldgraf@berkeley.edu>
#
# License: BSD (3-clause)
import matplotlib.pyplot as plt
import mne
from mne.datasets import sample
print(__doc__)
data_path = sample.data_path()
fname = data_path + '/MEG/sample/sa... |
1,777 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Visual Comparison Between Different Classification Methods in Shogun
Notebook by Youssef Emad El-Din (Github ID
Step1: <a id = "section1">Data Generation and Visualization</a>
Transformatio... | Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import os
SHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data')
from shogun import *
import shogun as sg
#Needed lists for the final plot
classifiers_linear = []*10
classifiers_non_linear = []*10
classifiers_names = []*10
fadings... |
1,778 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Finding similar documents with Word2Vec and WMD
Word Mover's Distance is a promising new tool in machine learning that allows us to submit a query and return the most relevant documents. For... | Python Code:
from time import time
start_nb = time()
# Initialize logging.
import logging
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s')
sentence_obama = 'Obama speaks to the media in Illinois'
sentence_president = 'The president greets the press in Chicago'
sentence_obama = sentence_obama.lowe... |
1,779 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<h1>Soundscape Analysis by Shift-Invariant Latent Components</h1>
<h2>Michael Casey - Bregman Labs, Dartmouth College</h2>
A toolkit for matrix factorization of soundscape spectrograms into ... | Python Code:
from pylab import * # numpy, matplotlib, plt
from bregman.suite import * # Bregman audio feature extraction library
from soundscapeecology import * # 2D time-frequency shift-invariant convolutive matrix factorization
%matplotlib inline
rcParams['figure.figsize'] = (15.0, 9.0)
Explanation: <h1>Soundscape An... |
1,780 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Drag from Tides
This adds a constant time lag model (Hut 1981) to tides raised on either the primary and/or the orbiting bodies.
As an example, we'll add the tides raised on a post-main sequ... | Python Code:
import rebound
import reboundx
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
def getsim():
sim = rebound.Simulation()
sim.units = ('yr', 'AU', 'Msun')
sim.add(m=0.86) # post-MS Sun
sim.add(m=3.e-6, a=1., e=0.03) # Earth
sim.move_to_com()
rebx = reboun... |
1,781 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Storage Commands
Google Cloud Datalab provides a set of commands for working with data stored in Google Cloud Storage. They can help you work with data files containing data that is not stor... | Python Code:
%%gcs --help
Explanation: Storage Commands
Google Cloud Datalab provides a set of commands for working with data stored in Google Cloud Storage. They can help you work with data files containing data that is not stored in BigQuery or manage data imported into or exported from BigQuery.
This notebook introd... |
1,782 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Continuous renal replacement therapy (CRRT)
This notebook overviews the process of defining CRRT
Step2: Step 1
Step4: The above gives us some hints to expand our initial search
Step6: Man... | Python Code:
# Import libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import psycopg2
from IPython.display import display, HTML # used to print out pretty pandas dataframes
import matplotlib.dates as dates
import matplotlib.lines as mlines
%matplotlib inline
plt.style.use('ggplot')
# s... |
1,783 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Quick introduction to GRASS GIS Temporal Framework
The GRASS GIS Temporal Framework implements temporal GIS functionality at user level and provides additionally an API to implement new spat... | Python Code:
import grass.temporal as tgis
import grass.script as gscript
Explanation: Quick introduction to GRASS GIS Temporal Framework
The GRASS GIS Temporal Framework implements temporal GIS functionality at user level and provides additionally an API to implement new spatio-temporal processing modules.
The tempora... |
1,784 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
DAT210x - Programming with Python for DS
Module5- Lab4
Step1: You can experiment with these parameters
Step2: Some Convenience Functions
Step3: Load up the dataset. It may or may not have... | Python Code:
import math
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib
from sklearn import preprocessing
from sklearn.decomposition import PCA
# You might need to import more modules here..
# .. your code here ..
matplotlib.style.use('ggplot') # Look Pretty
c = ['red', 'green'... |
1,785 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Magic functions
You can enable magic functions by loading pandas_td.ipython
Step1: It can be loaded automatically by the following configuration in "~/.ipython/profile_default/ipython_confi... | Python Code:
%load_ext pandas_td.ipython
Explanation: Magic functions
You can enable magic functions by loading pandas_td.ipython:
End of explanation
c = get_config()
c.InteractiveShellApp.extensions = [
'pandas_td.ipython',
]
Explanation: It can be loaded automatically by the following configuration in "~/.ipython... |
1,786 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Repaso (Módulo 1)
Recordar que el tema principal del módulo 1 son las ecuaciones diferenciales. Entonces, al finalizar este módulo, las competencias principales que deben tener ustedes es
- ... | Python Code:
# Numeral 1
# Importar librerías necesarias
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
# Definimos funcion mu
def mu(x, r):
return r*(1-x)
# Definimos conjunto de valores en x
x = np.linspace(0, 1.2, 50)
# Valor del parametro solicitado
r = 1
# Conjunto de valores en y
y = mu... |
1,787 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Table of Contents
<p><div class="lev1 toc-item"><a href="#Rotations" data-toc-modified-id="Rotations-1"><span class="toc-item-num">1 </span>Rotations</a></div><div class="lev1 toc... | Python Code:
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import xgboost as xgb
from sklearn.metrics import roc_curve, auc
from sklearn.metrics import precision_recall_curve
df = pd.read_csv("iris.csv")
Explanation: Table of Contents
<p><div class="lev1 toc-item"><a href=... |
1,788 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
User Defined Functions
User defined functions make for neater and more efficient programming.
We have already made use of several library functions in the math, scipy and numpy libraries.
St... | Python Code:
import numpy as np
import scipy.constants as constants
print('Pi = ', constants.pi)
h = float(input("Enter the height of the tower (in metres): "))
t = float(input("Enter the time interval (in seconds): "))
s = constants.g*t**2/2
print("The height of the ball is",h-s,"meters")
Explanation: User Defined Fun... |
1,789 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
In this short tutorial, we will build and expand on the previous tutorials by computing the dynamic connectivity, using Time-Varying Functional Connectivity Graphs.
In the near future, the s... | Python Code:
import numpy as np
import tqdm
raw_eeg_eyes_open = np.load("data/eeg_eyes_opened.npy")
raw_eeg_eyes_closed = np.load("data/eeg_eyes_closed.npy")
num_trials, num_channels, num_samples = np.shape(raw_eeg_eyes_open)
read_trials = 10
eeg_eyes_open = raw_eeg_eyes_open[0:read_trials, ...]
eeg_eyes_closed = raw_e... |
1,790 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
随机变量及其分布 Random Variable and its Distribution
包括以下内容：
1. 随机变量 Random Variable
2. 伯努利分布 Bernoulli Distribution
3. 二项分布 Binomial Distribution
4. 泊松分布 Poisson Distribution
5... | Python Code:
import math
import numpy as np
import pandas as pd
from pandas import Series, DataFrame
# 引入绘图包
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('whitegrid')
%matplotlib inline
Explanation: 随机变量及其分布 Random Variable and its Distribution
包括以下内容：
1. 随机变量 Random Variable
2. 伯努利分布 Ber... |
1,791 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
use spearman correlation between OTUs and 5 VitD variables (with BH FDR corrected p-val <= 0.05 as threshold)
use lasso regression on all OTUs vs. 5 VitD variables (need Cross-validation to ... | Python Code:
import warnings
warnings.filterwarnings("ignore")
import pandas as pd
import numpy as np
from scipy.stats import spearmanr, pearsonr
from statsmodels.sandbox.stats.multicomp import multipletests
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LassoLarsCV
from sklearn.p... |
1,792 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Chap 3 線形回帰 (ML)
問題設定
$N$ 個の観測値 ${\bf x}_n$, $(n=1, ..., N)$ とそれに対応する目標値 ${\bf t_n}$ のデータから
${\bf x}$ と ${\bf t}$ の関係をモデル化する。
線形回帰では、$M$ 個の重み係数 $w_j$, $(j=1, ..., M)$ と基底関数 ${\phi_j({\bf x})... | Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
Explanation: Chap 3 線形回帰 (ML)
問題設定
$N$ 個の観測値 ${\bf x}_n$, $(n=1, ..., N)$ とそれに対応する目標値 ${\bf t_n}$ のデータから
${\bf x}$ と ${\bf t}$ の関係をモデル化する。
線形回帰では、$M$ 個の重み係数 $w_j$, $(j=1, ..., M)$ と基底関数 ${\phi_j({\bf x})}$ の線形和
$$
y({\bf x}, {\bf w}) = \... |
1,793 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
가설검정
Step1: 오늘의 주요 예제
Step2: sp.factorial() 함수를 이용하여 조합의 경우의 수인 $\binom{n}{r}$을 계산하는 함수를 정의한다.
Step3: 이제 이항분포 확률를 구하는 함수는 다음과 같다.
n, r, p 세 개의 인자를 사용하며 p는 한 번 실행할 때 특정 사건이 발생할 확률이다.
Step4... | Python Code:
import numpy as np
from __future__ import print_function, division
Explanation: 가설검정
End of explanation
import sympy as sp
sp.factorial(5)
Explanation: 오늘의 주요 예제: 동전던지기
동전을 30번 던져서 앞면(Head)이 24번 나왔을 때, 정상적인 동전이라 할 수 있을까?
영가설(H0): 정상적인 동전이라면 30번 중에 보통은 15번은 앞면(Head), 15번은 뒷면(Tail)이 나온다.
따라서 정상적인 동전이 아니다.
대... |
1,794 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Computation
The labels associated with DataArray and Dataset objects enables some powerful shortcuts for computation, notably including aggregation and broadcasting by dimension names.
Basic... | Python Code:
%matplotlib inline
import numpy as np
import xarray as xr
arr = xr.DataArray(np.random.randn(2, 3), [('x', ['a', 'b']), ('y', [10, 20, 30])])
arr - 3
abs(arr)
Explanation: Computation
The labels associated with DataArray and Dataset objects enables some powerful shortcuts for computation, notably including... |
1,795 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Classification
Class MLPClassifier implements a multi-layer perceptron (MLP) algorithm that trains using Backpropagation.
MLP trains on two arrays
Step1: MLP can fit a non-linear model to t... | Python Code:
## Input
X = [[0., 0.], [1., 1.]]
## Labels
y = [0, 1]
## Create Model
clf = MLPClassifier(solver='lbfgs', alpha=1e-5,
hidden_layer_sizes=(5, 2), random_state=1)
## Fit
clf.fit(X, y)
## Make Predictions
clf.predict([[2., 2.], [-1., -2.]])
Explanation: Classification
Class MLPClassifier ... |
1,796 | Given the following text problem statement, write Python code to implement the functionality described below in problem statement
Problem:
I have a dataframe, e.g: | Problem:
import pandas as pd
df = pd.DataFrame({'Date': ['20.07.2018', '20.07.2018', '21.07.2018', '21.07.2018'],
'B': [10, 1, 0, 1],
'C': [8, 0, 1, 0]})
def g(df):
df1 = df.groupby('Date').agg(lambda x: (x%2==0).sum())
df2 = df.groupby('Date').agg(lambda x: (x%2==1).sum())... |
1,797 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
MultiGraph
Create 3 views
Step1: Code the user can supply to view the streaming data
Given the streams with the 3 different moving averages, create 3 separate views to obtain the data.
Step... | Python Code:
from streamsx.topology.topology import Topology
from streamsx.topology import context
from some_module import jsonRandomWalk, movingAverage
#from streamsx import rest
import json
# Define operators
rw = jsonRandomWalk()
ma_150 = movingAverage(150)
ma_50 = movingAverage(50)
# Define topology & submit
top = ... |
1,798 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
正規表現
20. JSONデータの読み込み
Wikipedia記事のJSONファイルを読み込み, 「イギリス」に関する記事本文を表示せよ.
問題21-29では, ここで抽出した記事本文に対して実行せよ.
Step1: 21. カテゴリ名を含む行を抽出
記事中でカテゴリ名を宣言している行を抽出せよ． | Python Code:
import pandas as pd
import json
def get_article(title):
for line in open('jawiki-country.json', 'r'):
data = json.loads(line)
if data['title'] == title:
return data['text'].split('\n')
England = get_article('イギリス')
print(type(England), England)
Explanation: 正規表現
20.... |
1,799 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Class Coding Lab
Step1: Part 1
Step2: Testing Out your API
The documentation for the API can be found here
Step3: Next we setup the headers and the rest is like calling any other API...
S... | Python Code:
# Run this to make sure you have the pre-requisites!
!pip install -q requests
# start by importing the modules we will need
import requests
import json
Explanation: Class Coding Lab: Web Services and APIs
Overview
The web has long evolved from user-consumption to device consumption. In the early days of t... |
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